This bottom‐up modeling study, supported by new population census 2011 data, simulates ozone (O₃) and fine particulate matter (PM_2.5) exposure on local to regional scales. It quantifies, present‐day premature mortalities associated with the exposure to near‐surface PM_2.5 and O₃ concentrations in India using a regional chemistry model. We estimate that PM_2.5 exposure leads to about 570,000 (CI95: 320,000–730,000) premature mortalities in 2011. On a national scale, our estimate of mortality by chronic obstructive pulmonary disease (COPD) due to O₃ exposure is about 12,000 people. The Indo‐Gangetic region accounts for a large part (~42%) of the estimated mortalities. The associated lost life expectancy is calculated as 3.4 ± 1.1 years for all of India with highest values found for Delhi (6.3 ± 2.2 years). The economic cost of estimated premature mortalities associated with PM_2.5 and O₃ exposure is about 640 (350–800) billion USD in 2011, which is a factor of 10 higher than total expenditure on health by public and private expenditure.

This bottom‐up modeling study, supported by emission inventories and crop production, simulates ozone on local to regional scales. It quantifies, for the first time, potential impact of ozone on district‐wise cotton, soybeans, rice, and wheat crops in India for the first decade of the 21st century. Wheat is the most impacted crop with losses of 3.5 ± 0.8 million tons (Mt), followed by rice at 2.1 ± 0.8 Mt, with the losses concentrated in central and north India. On the national scale, this loss is about 9.2% of the cereals required every year (61.2 Mt) under the provision of the recently implemented National Food Security Bill (in 2013) by the Government of India. The nationally aggregated yield loss is sufficient to feed about 94 million people living below poverty line in India.

Stable and clear atmospheric conditions, lower surface temperatures, an ample moisture supply, and a strong low-level inversion persisting for most of the night usually facilitates the formation of dense fog during winter in Delhi. This severely hinders the flight operations at India’s busiest airport, the Indira Gandhi International (IGI) Airport, where more than 900 flight operations occur per day and an interruption can cause significant financial losses to the aviation industry. It is important to undertake a quantitative study of the estimated losses. This study, undertaken for the first time in India, aimed to evaluate the impact of dense fog at IGI Airport on economic losses which occurred during the winter season between 2011 and 2016. The breakdown of charges for different segments of flight operations for the domestic and international sectors was obtained from India’s Ministry of Civil Aviation and the Center for Asia Pacific Aviation (CAPA) India. A total of 653 h of dense fog between 2011 and 2016 at IGI Airport caused economic losses of approximately 3.9 million USD (248 million Indian rupees) to the airlines. The analysis further found that from 2014–2015 onwards, there has been a reduction in the number of flight delays, diversions, and cancellations by approximately 88%, 55%, and 36%, respectively, due to the strict implementation of guidelines to facilitate the Category (CAT)-III landing for aircraft during dense fog.

This study reports a very high-resolution (400 m grid-spacing) operational air quality forecasting system developed to alert residents of Delhi and the National Capital Region (NCR) about forthcoming acute air pollution episodes. Such a high-resolution system has been developed for the first time and is evaluated during October 2019-February 2020. The system assimilates near real-time aerosol observations from in situ and space-borne platform in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to produce a 72-h forecast daily in a dynamical downscaling framework. The assimilation of aerosol optical depth and surface PM_2.5 observations improves the initial condition for surface PM_2.5 by about 45 µg/m³ (about 50%).The accuracy of the forecast degrades slightly with lead time as mean bias increase from + 2.5 µg/m³ on the first day to − 17 µg/m³ on the third day of forecast. Our forecast is found to be very skillful both for PM_2.5 concentration and unhealthy/ very unhealthy air quality index categories, and has been helping the decision-makers in Delhi make informed decisions.